How Does AI Store and “Know” Things?
AI models do not “know” things in the traditional human sense. They do not access a database of facts. Instead, they learn patterns. But for more advanced tasks, like answering questions based on your company’s documents or personalizing responses based on a user’s profile, models need a way to access external information. That is where vector-based systems come in. In this section, we explain how data is converted into AI-readable formats, stored, and retrieved in real time using embeddings, vectors, and vector stores. These are critical to many modern AI applications, especially tools that combine retrieval with generation.Embedding
What it means: An embedding is a numerical representation of a word, sentence, or document. It captures the meaning and context of the text in a form that an AI model can use for comparison. Analogy: Think of an embedding like a coordinate in space that represents the idea behind a piece of text. Words or phrases with similar meanings are close together in this space. Words like “Gas Station” and “Convenience Store” are not identical but are similar enough to be grouped together conceptually. Why it matters: Embeddings make it possible to compare concepts based on meaning rather than just exact words. How it shows up in Gloo: Gloo automatically converts uploaded content such as sermons, PDFs, curriculum, and articles into embeddings. These embeddings allow the Data Engine to understand meaning rather than just keywords, enabling accurate retrieval for Chat for Teams and Studio.Vector
What it means: A vector is the actual list of numbers that make up an embedding. It is what gets stored and compared in AI systems. Example: A short sentence like “I feel happy” might turn into a vector like [0.12, -0.45, 0.88, …]. These numbers don’t mean much on their own, but together they allow the system to measure similarity with other sentences. How it shows up in Gloo: Every piece of content processed by the Data Engine becomes a vector. These vectors are used internally to compare concepts, match user questions to relevant content, and power high quality search and RAG responses.Vector Store (or Vector Database)
What it means: A vector store is a special kind of database that stores embeddings (vectors) and can quickly find the ones that are most similar to a new query. Use case: Let’s say you ask, “What’s our return policy?” The system turns your question into a vector, searches the database for similar content, and retrieves the relevant documents. The model can then generate a response based on those results. Popular tools: Pinecone, Weaviate, FAISS, and Qdrant are examples of vector databases commonly used with AI systems. How it shows up in Gloo: Gloo maintains a secure, rights respecting vector store (with a continually improving feature set) that organizes an organization’s content. This vector store allows the system to retrieve the most relevant passages, scriptures, or policy documents when users ask questions in Chat for Teams or Studio.Semantic Search
What it means: Semantic search is the process of searching based on meaning, not keywords. It uses embeddings and vector similarity instead of traditional keyword matching. Example: If you search “how do I get a refund,” the system might return a document titled “return policy” even though the exact word “refund” isn’t used. How it shows up in Gloo: All Gloo search and retrieval is semantic. The platform matches user questions with content based on meaning, not exact keywords. This makes it possible for staff and volunteers to ask natural questions and still receive accurate, grounded answers, and for developers to make valuable experiences for their users.Retrieval-Augmented Generation (RAG)
What it means: RAG is a technique where a model retrieves specific relevant documents before generating a response. It combines search and generation in one system. Why it matters: RAG allows models to stay up-to-date or domain-specific without retraining. The model brings in outside context, then writes its answer based on that. Use case: In a church outreach setting, RAG could allow an AI tool to answer questions using curated content from your organization’s internal materials, even if that content wasn’t part of the model’s original training data. For example this could allow your site to have a search feature that could return specific snippets of past sermons or answer a volunteer’s question about specific policies. How it shows up in Gloo: RAG is at the core of many of Gloo’s platform offerings. When a user asks a question, the system retrieves the most relevant documents from the organization’s content, then generates a response grounded in that material. This ensures alignment, accuracy, and trustworthiness without fine tuning.Knowledge Base
What it means: A knowledge base is the collection of documents or facts that a system pulls from. When combined with embeddings and a vector store, it becomes searchable and usable by an AI. Why it matters: This is the human-curated layer of information that brings reliability, structure, and context to AI responses. How it shows up in Gloo: An organization’s uploaded content becomes its private knowledge base inside Gloo. Studio helps manage this content with tools like enrichment, insights, and DRM. Chat for Teams uses the knowledge base to respond with accurate, organization specific information.Enrichment
What it means: Enrichment refers to adding metadata, structure, or additional tags to documents before or after embedding. This helps improve retrieval and relevance. Example: Before storing a sermon transcript, you might add the speaker’s name, date, Bible passage, and topic as enrichment fields. When users ask questions, the AI can prioritize the most relevant enriched results. How it shows up in Gloo: Gloo enriches content as requested per customer by extracting topics, themes, scripture references, and metadata. These enriched fields improve retrieval accuracy and help structure content for ministry workflows.Next Up: What Are Agents and How Do They Make AI More Capable? In the next section, we’ll answer: “What are AI agents, and how do they plan, act, and work across tasks like humans do?”